Applications of Artificial Intelligence (AI) in Energy Storage Systems Design, Operation and Control

Dear Colleagues,

Energy storage systems (ESSs) are receiving growing attention as main stream solutions for the widespread use of renewable energies and subsequently as a means of decarbonizing the electrification of society. At the building scale, they are increasingly utilized to enhance heat and cooling energy recovery, promote distributed energy supply and improve the efficiency of cities in reducing their peak demands by helping the utility companies to maintain reliable and resilient generation and distribution capacities. As energy storage systems are well-positioned to bridge the inputs from renewable and recovered energies with the energy demand across varied scales, geographies, and times, there is a pressing need to expand the research in systems’ modeling and analysis of energy storage technologies and their applications. This is of particular importance due to the various types of energy storage technologies in varied sizes and scales, and their diverse operational characteristics and challenges with respect to efficiency and reliability. In doing so, artificial intelligence provides an opportunity to better adapt energy storage systems with changing environmental conditions, dynamic characteristics of the grid, intermittent nature of renewables, thus improving the reliability and resilience of these systems. AI is widely applied in the sizing, scheduling, control, and optimization of energy systems. This Special Issue intends to collect and disseminate the state of the art on research and practice in applications of AI in modeling and analysis of energy storage systems with a focus on the following (and other closely related) topics:

• Energy supply predictions for integration of renewable energies and ESSs;
• Energy demand and critical load predictions for ESSs control and operation;
• Capacity planning and sizing of ESSs;
• Optimized scheduling of ESSs;
• Monitoring and control of ESSs;
• Fault detection, diagnosis, and reliability analysis for ESSs;
• Distributed management of ESSs;
• Data analytics for life cycle analysis of ESSs;
• Location-specific systems analysis for ESSs.

Dr. Fuzhan Nasiri
Prof. Dr. Ryozo Ooka
Prof. Dr. Fariborz Haghighat
prof. Dr. Ruchi Choudhary
Prof. Dr. Frédéric Kuznik
Prof. Dr. Mariagrazia Dotoli
Prof. Dr. Alireza Afshari
Dr. Enrico Fabrizio
Guest Editors

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